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22 Documents (Liste longue)

Self-RAG: Learning to Retrieve, Generate and Critique through Self-Reflection

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2023-10-20 A propos

[2307.13269] LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

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2023-08-08 A propos

[2303.17651] Self-Refine: Iterative Refinement with Self-Feedback

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2023-04-03 A propos

[2303.14177] Scaling Expert Language Models with Unsupervised Domain Discovery

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2023-03-27 A propos

[2212.09741] One Embedder, Any Task: Instruction-Finetuned Text Embeddings

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2023-02-17 A propos

Embedding Recycling: Making Language Model Development More Sustainable | AI2 Blog

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2023-02-17 A propos

[2209.01975] Selective Annotation Makes Language Models Better Few-Shot Learners

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2022-09-07 A propos

[2106.10199] BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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2022-09-01 A propos

[1902.06006] Contextual Word Representations: A Contextual Introduction

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2022-07-08 A propos

[2205.08012] CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction

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2022-07-07 A propos

(((ل()(ل() 'yoav))))👾 sur Twitter : "... another step in understanding how transformer-based LMs work..."

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2022-03-30 A propos

SPIKE: Extractive Search from Allen Institute for AI

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2022-02-22 A propos

[2107.12708] QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

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2021-08-06 A propos

[2106.04612] Neural Extractive Search

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how to extend a
search paradigm we call “**extractive search**” with
neural similarity techniques.

> some information needs require extracting
and aggregating sub-sentence information
(words, phrases, or entities) from multiple documents
(e.g. a list of all the risk factors for a specific
disease and their number of mentions, or a comprehensive
table of startups and CEOs).

> extractive search combines
document selection with information extraction. **The query is extended with capture slots**:
these are **search terms that act as variables, whose
values should be extracted**.
> The user
is then presented with the matched documents, each
annotated with the corresponding captured spans,
as well as aggregate information over the captured
spans

Conclusion :

> We presented a system for neural extractive search.
While we found our system to be useful for scientific
search, it also has clear limitations and areas
for improvement, both in terms of accuracy (only
72.2% of the returned results are relevant, both the
alignment and similarity models generalize well to
some relations but not to others), and in terms of
scale

[Video of demo](https://www.youtube.com/watch?v=TtqWi2GgB5A&t=1832s)

2021-06-23 A propos

[1807.04905] Ultra-Fine Entity Typing

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2021-06-22 A propos

Semantic Scholar | TLDR Feature

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2020-11-24 A propos

TL;DR: This AI summarizes research papers so you don’t have to

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2020-11-24 A propos

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 A propos

[1906.07241] Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

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2020-05-11 A propos

Isabel Cachola sur Twitter : "TLDR: Extreme Summarization of Scientific Documents"

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2020-05-03 A propos

[2004.05150] Longformer: The Long-Document Transformer

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2020-04-13 A propos

[2002.05867] Transformers as Soft Reasoners over Language

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2020-02-17 A propos

Propriétés

sl:creationDate : 2020-02-17
sl:creationTime : 2020-02-17T09:09:17Z
rdf:type : sl:Tag
skos:altLabel : AllenAI
skos:prefLabel : Allen Institute for AI (AI2)
foaf:homepage : https://allenai.org